feat(iit): Complete CRA Agent V3.0 P1 - ChatOrchestrator with LLM Function Calling

P1 Architecture: Lightweight ReAct (Function Calling loop, max 3 rounds)

Core changes:
- Add ToolDefinition/ToolCall types to LLM adapters (DeepSeek + CloseAI + Claude)
- Replace 6 old tools with 4 semantic tools: read_report, look_up_data, check_quality, search_knowledge
- Create ChatOrchestrator (~160 lines) replacing ChatService (1,442 lines)
- Wire WechatCallbackController to ChatOrchestrator, deprecate ChatService
- Fix nullable content (string | null) across 12+ LLM consumer files

E2E test results: 8/8 scenarios passed (100%)
- QC report query, critical issues, patient data, trend, on-demand QC
- Knowledge base search, project overview, data modification refusal

Net code reduction: ~1,100 lines
Tested: E2E P1 chat test 8/8 passed with DeepSeek API

Made-with: Cursor

backend/src/modules/ssa/services/ReflectionService.ts

@@ -104,7 +104,7 @@ export class ReflectionService {
         maxTokens: LLM_MAX_TOKENS,
       });
 
-      const rawOutput = response.content;
+      const rawOutput = response.content ?? '';
       logger.info('[SSA:Reflection] LLM response received', {
         contentLength: rawOutput.length,
         usage: response.usage,